{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import norm,t,laplace\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize']=(15,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'(b)')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 产生服从正态分布的随机数\n",
    "x=norm.rvs(loc=0,scale=1,size=100)\n",
    "noise = np.array([8,9,10])\n",
    "x_noise = np.append(x,noise)\n",
    "bins=np.arange(-5,12)\n",
    "ax1=plt.subplot(121)\n",
    "ax2=plt.subplot(122)\n",
    "\n",
    "result1=ax1.hist(x,bins,density=True,align='left',rwidth=0.7,facecolor='red')[0]\n",
    "loc_norm,scale_norm=norm.fit(x)\n",
    "df,loc_t,scale_t=t.fit(x)\n",
    "loc_laplace,scale_laplace = laplace.fit(x)\n",
    "\n",
    "result2=ax2.hist(x_noise,bins,density=True,align='left',rwidth=0.7,facecolor='red')[0]\n",
    "loc_norm2,scale_norm2=norm.fit(x_noise)\n",
    "df2,loc_t2,scale_t2=t.fit(x_noise)\n",
    "loc_laplace2,scale_laplace2 = laplace.fit(x_noise)\n",
    "\n",
    "# 绘制拟合后的曲线\n",
    "x_norm = np.linspace(norm.ppf(0.001,loc=loc_norm,scale=scale_norm),\n",
    "                    norm.ppf(0.999,loc=loc_norm,scale=scale_norm),1000)\n",
    "ax1.plot(x_norm,norm.pdf(x_norm,loc=loc_norm,scale=scale_norm),color='black',linewidth=2,ls='-',label='Gaussian')\n",
    "\n",
    "x_t = np.linspace(t.ppf(0.001,df=df,loc=loc_t,scale=scale_t),\n",
    "                    t.ppf(0.999,df=df,loc=loc_t,scale=scale_t),1000)\n",
    "ax1.plot(x_t,t.pdf(x_t,df=df,loc=loc_t,scale=scale_t),color='blue',linewidth=2,ls='-.',label='Student')\n",
    "\n",
    "x_laplace = np.linspace(laplace.ppf(0.001,loc=loc_laplace,scale=scale_laplace),\n",
    "                    norm.ppf(0.999,loc=loc_laplace,scale=scale_laplace),1000)\n",
    "ax1.plot(x_laplace,laplace.pdf(x_laplace,loc=loc_laplace,scale=scale_laplace),color='green',linewidth=2,ls='--',label='laplace')\n",
    "\n",
    "x_norm2 = np.linspace(norm.ppf(0.001,loc=loc_norm2,scale=scale_norm2),\n",
    "                    norm.ppf(0.999,loc=loc_norm2,scale=scale_norm2),1000)\n",
    "ax2.plot(x_norm2,norm.pdf(x_norm2,loc=loc_norm2,scale=scale_norm2),color='black',linewidth=2,ls='-',label='Gaussian')\n",
    "\n",
    "x_t2 = np.linspace(t.ppf(0.001,df=df2,loc=loc_t2,scale=scale_t2),\n",
    "                    t.ppf(0.999,df=df2,loc=loc_t2,scale=scale_t2),1000)\n",
    "ax2.plot(x_t2,t.pdf(x_t2,df=df2,loc=loc_t2,scale=scale_t2),color='blue',linewidth=2,ls='-.',label='Student')\n",
    "\n",
    "x_laplace2 = np.linspace(laplace.ppf(0.001,loc=loc_laplace2,scale=scale_laplace2),\n",
    "                    norm.ppf(0.999,loc=loc_laplace2,scale=scale_laplace2),1000)\n",
    "ax2.plot(x_laplace2,laplace.pdf(x_laplace2,loc=loc_laplace2,scale=scale_laplace2),color='green',linewidth=2,ls='--',label='laplace')\n",
    "\n",
    "ax1.set_ylim((0,0.6))\n",
    "ax2.set_ylim((0,0.6))\n",
    "ax1.set_xlim((-6,11))\n",
    "ax2.set_xlim((-6,11))\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "ax1.set_xlabel('(a)')\n",
    "ax2.set_xlabel('(b)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
